a scrapbook
Friday 08 May 2015
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-https://nsaunders.wordpress.com/2015/03/24/pubmed-retraction-reporting-update/
Note, the next two clippings are from newspapers, not scientific journals
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The article presented broad-scale patterns of species distribution shifts in response to recent climate change. Unfortunately, it has since been found that one approach used to account for sampling bias, the null model approach, was affected by the coding error.
http://www.nature.com/news/rule-rewrite-aims-to-clean-up-scientific-software-1.17323 (with thanks to Rob Schick)
Hadley Wickham, Simply Statistics Unconference http://t.co/D931Og8mq3
Hadley Wickham, Simply Statistics Unconference http://t.co/D931Og8mq3
http://www.quora.com/What-is-data-munging
I had to explain this concept to students
http://simplystatistics.org/2015/03/13/de-weaponizing-reproducibility/
The Academy of Medical Sciences, jointly with the BBSRC, MRC and Wellcome Trust, held a symposium on 1-2 April 2015 to explore the challenges and opportunities for improving the reproducibility and reliability of biomedical research in the UK.
Obtain an estimate of probability of occupancy per site \((\Psi)\), together with an approximate 95% confidence interval for this probability, assuming perfect detection of the species within each site.
library(RMark, quietly=TRUE)
data(Donovan.7)
N.total <- dim(Donovan.7)[1]
T.occ <- nchar(Donovan.7$ch[1])
n.occupied <- sum(Donovan.7$ch!="00000")
Psi.0 <- n.occupied/N.total
Psi.0.ci <- Psi.0 + c(-1.96,1.96)*sqrt(Psi.0*(1-Psi.0)/N.total) # assuming normality
Based upon 17 of the 20 sites occupied, the estimate of occupancy is 0.85 with a confidence interval (assuming normality) of (0.694, 1.006).
Without fitting a model, decide whether the maximum likelihood estimates of an occupancy model fitted to these data assuming constant detection probability p, satisfy these equations:
\[\hat{\Psi} = \frac{n}{N\hat{p_.}} \]
\[\frac{\hat{p}}{\hat{p_.}} = \frac{\delta_{..}}{nT}\]
An increasing number of initiatives aim to encourage scientists to ensure that their software is replicable. Courses run by organizations such as the non-profit Software Carpentry Foundation teach the value of writing and sharing solid scientific code, as well as the principles of constructing it. Software packages such as iPython and knitr make it easier to document code creation transparently and in its research context.
http://www.nature.com/news/rule-rewrite-aims-to-clean-up-scientific-software-1.17323
One proposal coming from the National Academy workshop:
Leek and Peng 2015: Proceedings of National Academy of Science http://www.pnas.org/cgi/doi/10.1073.pnas.1421412111